{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b1589ad-eeff-4590-b235-c7882ff16ee3",
   "metadata": {},
   "source": [
    "# With TrivialAugment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "808b53e6-93b7-4cbc-bfe4-ce87ef3a95de",
   "metadata": {},
   "source": [
    "This notebook trains a ResNet-18 model with the AutoAugment procedure on CIFAR-10. \n",
    "\n",
    "Compare the performance to the baseline [./without-augmentation.ipynb](./without-augmentation.ipynb).\n",
    "\n",
    "\n",
    "## References\n",
    "\n",
    "AutoAugment paper:\n",
    "- [TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation](https://arxiv.org/abs/2103.10158)\n",
    "\n",
    "PyTorch/torchvision documentation:\n",
    "- [transforms.TrivialAugmentWide](https://pytorch.org/vision/main/generated/torchvision.transforms.TrivialAugmentWide.html)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4af58769",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch      : 1.13.1\n",
      "lightning  : 1.9.0\n",
      "torchvision: 0.14.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning,torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4363916",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import time\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchmetrics\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "from helper_utilities import LightningModel,Cifar10DataModule,plot_loss_and_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66486cc5-7c7e-497c-bfb9-fc5e918256f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 123\n"
     ]
    }
   ],
   "source": [
    "from torchvision import transforms\n",
    "\n",
    "train_transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(32),\n",
    "        transforms.TrivialAugmentWide(),\n",
    "        transforms.ToTensor(),\n",
    "        \n",
    "    ]\n",
    ")\n",
    "L.seed_everything(123)\n",
    "dm = Cifar10DataModule(\n",
    "    height_width=(32, 32),\n",
    "    batch_size=256, \n",
    "    train_transform=train_transform, \n",
    "    num_workers=4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cd186d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n",
      "\n",
      "  | Name      | Type               | Params\n",
      "-------------------------------------------------\n",
      "0 | model     | ResNet             | 11.2 M\n",
      "1 | train_acc | MulticlassAccuracy | 0     \n",
      "2 | val_acc   | MulticlassAccuracy | 0     \n",
      "3 | test_acc  | MulticlassAccuracy | 0     \n",
      "-------------------------------------------------\n",
      "11.2 M    Trainable params\n",
      "0         Non-trainable params\n",
      "11.2 M    Total params\n",
      "44.727    Total estimated model params size (MB)\n"
     ]
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=1000` reached.\n"
     ]
    }
   ],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "pytorch_model = torchvision.models.resnet18(weights=None)\n",
    "pytorch_model.fc = torch.nn.Linear(512, 10)\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.05)\n",
    "\n",
    "\n",
    "start_1 = time.time()\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=1000,\n",
    "    accelerator=\"gpu\",\n",
    "    devices=[1],\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"trivialaugment\"),\n",
    "    deterministic=True,\n",
    ")\n",
    "\n",
    "trainer.fit(model=lightning_model, datamodule=dm)\n",
    "\n",
    "end_1 = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "011bd890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 98.82 min\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Training time: {(end_1-start_1)/60:.2f} min\")\n",
    "\n",
    "plot_loss_and_acc(\n",
    "    trainer.logger.log_dir, \n",
    "    acc_ylim=(0.3, 1.1), \n",
    "    save_loss=\"1_trivialaugment-loss.pdf\", \n",
    "    save_acc=\"1_trivialaugment-acc.pdf\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f38e1c7-10de-4184-9e99-2c30f89097a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cf7cbff91864a9a9137e882764ff5a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8167999982833862     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8167999982833862    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.8167999982833862}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=dm)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
